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Dense RFID environments pose critical challenges such as Reader-to-Reader Interference (RRI), Reader-to-Tag Collisions (RTC), and inefficient resource utilization, which degrade system performance and scalability. Traditional Media Access Control (MAC) protocols, including CSMA and TDMA, struggle to address these issues effectively, particularly in dynamic and large-scale deployments. This paper introduces MCSMARA (Markov Decision Process (MDP)-based Carrier Sense Multiple Access with Reader Arbitration), a novel MAC protocol designed to optimize reader coordination in dense RFID networks. By leveraging an MDP framework, MCSMARA models reader state transitions and employs a utility-based arbitration mechanism to dynamically allocate frequencies and time slots. The protocol incorporates adaptive backoff and decentralized neighborhood discovery for efficient resource management without centralized control. Simulation results demonstrate that MCSMARA reduces collisions by up to 30%, improves throughput by 25%, and ensures superior scalability, supporting a large amount of readers with minimal computational overhead. These findings establish MCSMARA as a transformative solution for RFID networks in logistics, retail, and industrial IoT, with potential for extension to mobile and heterogeneous environments. 

In dense RFID systems, efficient coordination of multiple readers is crucial to prevent reader-to-reader interference (RRI) and ensure optimal system performance. As the number of readers and tags increases, static frequency and time-slot assignment become insufficient to handle dynamic network conditions, leading to collisions, missed tag reads, and degraded throughput. In this paper, we propose a decentralized neigh-borhood discovery and management scheme for RFID systems operating in high-density environments. Our approach minimizes interference and improves tag read accuracy by dynamically adjusting communication parameters like frequency and time slots based on current system conditions, which are updated by periodic information exchanges among readers. Experimental results demonstrate that the proposed method significantly improves system scalability, throughput, and reliability. The proposed framework offers a scalable and adaptive solution for dense reader environments. 

This paper presents the MAE model that uses a Masked AutoEncoder (MAE) to enhance the observations from commercial passive Radio-Frequency Identification (RFID) devices. It is crucial to address the common issue of RFID readers failing to collect observations from all their hop channels and antennas due to environmental effects and device limitations. The proposed method examines the inner rationale among observations from various channels and antennas to reconstruct the missing observations, which can significantly improve the performance of downstream applications. The experiment results show that when we collect more than 70% observation in all antennas at all channels, our MAE model can restore 90% of the missing phase with an error of less than 0.1 radians, which is even less than the error caused by thermal noise in an RFID system. Our MAE model's accuracy in restoring missing data provides a promising future to improve the performance of various RFID applications like localization and motion tracking by providing more complete observations. 

In dense RFID systems, power control provides an effective means for maintaining communication efficiency and preventing reader-to-reader and reader-to-tag interference. Traditional RFID systems often operate at fixed power levels, which can lead to communication bottlenecks and inefficient tag reads in dynamic environments. This paper proposes an adaptive power control technique to improve the system performance by dynamically adjusting the transmit power based on environmental conditions, tag distance, and network congestion. Simulations and experimental results demonstrate that the proposed approach improves tag read rates, reduces interference, and enhances system robustness in dense environments. 

In this paper, we present a conformal prediction (CP) based method to evaluate the performance of a finger-printing localization system through uncertainty quantification. The proposed method emphasizes a standalone module that is compatible with any well-trained fingerprint classifier without incurring extra training costs. It provides rigorous statistical guarantees for revealing true labels in the fingerprinting multi-class classification problems with high efficiency. Uncertainty quantification of the predictions is accomplished by leveraging a small calibration dataset and a given error tolerance level. Three specific metrics are introduced to quantify the uncertainty of the CP-based method from the perspective of efficiency, adaptivity, and accuracy, respectively. The proposed method allows developers to track the model state with minimal effort and evaluate the reliability of their model and measurements, such as in a dynamic environment. The proposed technique, therefore, prevents the intrinsic label inaccuracy and the additional labor cost of ground truth collection. We evaluate the proposed method and metrics in two representative indoor environments using vanilla fingerprint-based localization models with extensive experiments. Our experimental results show that the proposed method can successfully quantify the uncertainty of predictions.